Jakob Nordström
Photo: Kennet Rouna

Email: 
jn atsign di dot ku dot dk or 

jakob dot nordstrom atsign cs dot lth dot se 


Cellular:

+46 (0)70 742 21 98
/
+45 28 78 38 11



Address in Copenhagen:

Datalogisk Institut, Københavns Universitet (DIKU)
Algorithms & Complexity Section
Universitetsparken 1, office 3109
2100 Copenhagen, DENMARK

Address in Lund:

Institutionen för datavetenskap
Lunds universitet
Ole Römers väg 3, office 2130a
221 00 Lund, SWEDEN


News

The Department of Computer Science at the University of Copenhagen
invites applications
from
professors at all levels within all areas of computer science.
Needless to say, applicants in algorithms and complexity theory are particularly welcome!
😀
The application deadline is
September 1, 2024.

I am looking for a PhD student
who will help us go even further beyond the state of the art in
provably correct computation with certifying algorithms (as per this
tutorial)
or possibly in combinatorial solving and optimization (as explained in this
tutorial).
This is a pretty unique opportunity if you have a
strong background in both mathematics and programming and wish to combine the two.
The application deadline is
September 6, 2024.

I am helping to organize the
PseudoBoolean Competition 2024
as part of the
International Conference on
Theory and Applications of Satisfiability Testing
in August 2024.

In May 2024, I arranged the
1st International Workshop on Highlights
in Organizing and Optimizing Prooflogging Systems
(WHOOPS '24)
at the University of Copenhagen.

In March 2024
I organized the workshop
Proof Complexity and Beyond
at
Mathematisches Forschungsinstitut Oberwolfach
together with
Albert Atserias,
Meena Mahajan,
and
Alexander Razborov.

At
the constraint programming conference
CP '23 in August 2023, former MIAO PhD
student Stephan Gocht
received the ACP Doctoral Research Award
from the Association for Constraint Programming for his work on
proof logging. Not only that, but our close
collaborator Ciaran McCreesh
received the ACP Early Career Researcher Award,
among other things for our joint work on proof logging, and Ciaran and
his PhD student Matthew McIlree
got the CP 2023 best paper award for their work
on proof logging for constraint programming solvers using
VeriPB. So,
all in all, a pretty good week for certifying combinatorial
solvers… Big congrats to Stephan, Ciaran, and Matthew!

At
the AI conference
IJCAI '23
in August 2023, I gave a tutorial
Combinatorial Solving with Provably Correct Results
on
VeriPB
proof logging joint with
Bart Bogaerts
and
Ciaran McCreesh.
(See the
VeriPB YouTube tutorial from the autumn of 2022
with
accompanying slides
for an earlier version.)

The SAT solver with
VeriPB
proof logging, BreakIDkissatlow,
that we submitted to the
SAT Competition 2023
was the most successful solver in the competition, and would have won 1st prize
had the competition rules been like in previous years.
With VeriPB proof logging we can support advanced SAT
solving techniques that have previously been out of reach for the
standard SAT proof logging format, thus increasing the power of the
solver exponentially. However, our proof checking is still not as
fast as in the highly optimized checkers for the standard format
that have been used for the last decade, and some of our proofs
were not verified within the required time limits.
Therefore, we instead had to to content ourselves with receiving
an honorary 1st prize.
We know how to make our toolchain for formal verification of
proofs more efficient, though, and are working on it for next
year—and it is already clear what is the future of efficient,
powerful, fully general SAT proof logging…
Academic affiliation and background
I am a professor at the
Department of Computer Science
at the
University of Copenhagen,
Denmark,
and also have a parttime affiliation with the
the
Department of Computer Science
at
Lund University.
Prior to moving to Copenhagen and Lund, I worked at
KTH Royal Institute of Technology
as an assistant professor and then associate
professor during the years 20112019.
During 20082010
I was a postdoc at the
Computer Science and
Artificial Intelligence Laboratory
at the
Massachusetts Institute of Technology
hosted by
Madhu Sudan.
Before that I was a PhD student of
Johan Håstad
in the
Theory Group
at
KTH.
where I defended my PhD thesis in 2008.
Please see my biographic sketch for more information.
About my research
Computers are everywhere today—at work, in our cars, in our
living rooms, and even in our pockets—and have changed the world
beyond our wildest imagination. Yet these marvellous devices are, at
the core, amazingly simple and stupid: all they can do is to
mechanically shuffle around zeros and ones. What is the true
potential of such automated computational devices? And what are the
limits of what can be done by mindless calculations? Finding answers to
this kind of questions is ultimately what my research is about.
Computational complexity theory gives these deep
and fascinating philosophical questions a crisp mathematical
meaning. A computational problem is any task that is in principle
amenable to being solved by a computer—i.e., it can be worked out
by mechanical application of mathematical steps. By constructing
general, abstract models of computers we can study how to design
efficient methods, or algorithms, for solving different tasks, but
also prove mathematical theorems showing that some computational
problems just cannot be solved efficiently for inherent reasons
(meaning that is impossible to design algorithms for them that are as efficient as we would like).
I am particularly interested in understanding combinatorial
optimization problems, which are of fundamental mathematical
importance but also have wideranging applications in industry. My
goal is, one the one hand, to prove formally that many such problems
are beyond the reach of current algorithmic techniques, but also, on
the other hand, to develop new algorithms that have the potential to
go significantly beyond the current state of the art.
Recently, I have also been doing research on how complexity theory
can be harnessed to produce certificates that algorithms are
actually computing correct results. It is an open secret in
combinatorial optimization that even the best commercial tools
sometimes produce wrong answers, but there has been no really
principled way of addressing this problem.
We are working on changing this.
See the
presentation of my research group
for more information.
Some links
